Northern Australian temperature outlook
The chances of the summer maximum and minimum temperature exceeding the long-term median are greater than 60% over eastern Australia and the Top End of the NT (see map). Odds increase to greater than 70% over the Cape York Peninsula and southeast Queensland for both maximum and minimum temperatures. While warmer temperatures for these regions are not the only possible scenario for these regions, it is the most likely. The odds suggest that for every ten summer outlooks with odds similar to these, about six or seven of them would be expected to be warmer than average over these areas, while about three or four years would be cooler.
The tropical Pacific has remained ENSO-neutral since mid-2012. The dynamical seasonal outlook model suggests ENSO-neutral conditions will remain at least for the next three months. This means there is no strong shift in the odds from the tropical Pacific in this outlook.
With the main climate influences likely to remain neutral (and hence have lesser impact upon Australia) over the coming months, secondary influences, such as sea surface temperature patterns, persistent pressure systems, and changes in wind patterns around the Australian continent are tending to drive the Australian climate. While these shorter-term fluctuations are less predictable, the model still shows reasonable skill in seasonal temperature forecasts for most of northern Australia.
How accurate is the outlook?
Outlook accuracy is related to how consistently the oceans and broadscale climate affect Australian temperatures. During the December to February period, historical accuracy shows the outlook for maximum temperatures to be moderately consistent over most of northern Australia, except for the central NT which is only weakly consistent.
The effect on minimum temperatures during this season is moderately consistent over most of the north, excluding parts of the central NT, and parts of western Queensland, which are only very weakly consistent.
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Outlook confidence (or accuracy) is measured by comparing how often the outlook favoured a particular category (for instance, when above median rainfall was more likely to occur than below median rainfall in a particular season), and that the more likely category was then subsequently observed. This measurement of skill is known as "Percent Consistent", and has been tested over the period from 1981 to 2010.
Strong consistency means that tests of the model on historical data show a strong relationship between the most likely outlook category (above/below median) and the verifying observation (above/below median). In areas with strong consistency, relatively high confidence can be placed in future outlook probabilities. Very weak consistency means the historical relationship, and therefore outlook confidence, is low. In the places and seasons where the outlooks are most skilful, the category of the eventual outcome (above or below median) is consistent with the category favoured in the outlook about 75% of the time. In the least skilful areas, the outlooks perform no better than chance.
A random forecast of above median rainfall will be correct about 50% of the time. For this reason, the green shading on the map shows areas where the model has greater than 50% accuracy only. In areas which are not coloured in green on the map, some caution should be taken when using the forecast, notably at times when there is not a strong driver of our climate (e.g., no El Niño or La Niña is present; for commentary on the state of the main climate drivers, please see our ENSO Wrap Up).
The skill at predicting seasonal maximum temperature is good for most of the year, with the lowest point during the winter seasons. Of the variables predicted (i.e. rainfall, and maximum and minimum temperature), maximum temperature performs best. The skill at predicting seasonal minimum temperature peaks during summer, late autumn and late spring. Skill is lowest during late summer and late winter.
What is normal for this period?
These maps show the median (or 50th percentile) maximum and minimum temperature for the given three months. The median temperatures are calculated from the 1981-2010 period.
The maps will differ from other median maps on the Bureau's website. This is because the dynamical model forecasts use an averaging period of 1981-2010. The quality of the dynamical model forecasts is in-part determined by the coverage and accuracy of the observations fed into it. Therefore, to be consistent from one year to the next, the Bureau has only run the model during the modern satellite era.
About the outlook
Using the outlook
The Bureau's rainfall seasonal climate outlooks are general statements about the likelihood of wetter or drier than average weather over a three-month period. The probabilities are generated from the Predictive Ocean Atmosphere Model for Australia (POAMA), the Bureau of Meteorology's dynamical climate model. It is important to note that they are not categorical predictions about future rainfall, and hence the success or failure of one individual outlook does not infer that the model has low skill. Skill is assessed over multiple runs of the model. Likewise, temperature outlooks give the likelihood or chance of exceeding the average maximum and minimum temperatures over the entire three-month outlook period. Information about whether individual weeks or months may be unusually hot or cold, is presently unavailable.
Probability outlooks should not be used as if they were categorical (yes/no) forecasts. These outlooks should be used as a tool in risk management and decision making. Greatest benefits accrue from long-term use, say over 10 years.
About the model
The seasonal climate outlooks are generated by the Predictive Climate Ocean Atmosphere Model for Australia (POAMA), a dynamical (physics based) climate model developed by the Bureau of Meteorology and CSIRO Marine and Atmospheric Research. This coupled atmosphere-ocean model is a state of the art seasonal forecast system. Read more about POAMA.
The POAMA model is undergoing continuous research and development. Advances in the science of seasonal prediction, improvements in the observations and how they are fed into the model, as well as increases in supercomputing power are just some of the ways the model's accuracy will increase over time.
El Niño and La Niña
Indian Ocean Dipole
Statistical model outlooks
The official dynamical outlooks supercede the statistical outlooks. Statistical outlook maps will continue to be available for a review period: